Unintentional Security Flaws in Code: Automated Defense via Root Cause Analysis

• URL: http://arxiv.org/abs/2409.00199v1
• Date: Fri, 30 Aug 2024 18:26:59 GMT
• Title: Unintentional Security Flaws in Code: Automated Defense via Root Cause Analysis
• Authors: Nafis Tanveer Islam, Mazal Bethany, Dylan Manuel, Murtuza Jadliwala, Peyman Najafirad,
• Abstract summary: We developed an automated vulnerability root cause (RC) toolkit called T5-RCGCN. It combines T5 language model embeddings with a graph convolutional network (GCN) for vulnerability classification and localization. We tested T5-RCGCN with 56 junior developers across three datasets, showing a 28.9% improvement in code security compared to previous methods.
• Score: 2.899501205987888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software security remains a critical concern, particularly as junior developers, often lacking comprehensive knowledge of security practices, contribute to codebases. While there are tools to help developers proactively write secure code, their actual effectiveness in helping developers fix their vulnerable code remains largely unmeasured. Moreover, these approaches typically focus on classifying and localizing vulnerabilities without highlighting the specific code segments that are the root cause of the issues, a crucial aspect for developers seeking to fix their vulnerable code. To address these challenges, we conducted a comprehensive study evaluating the efficacy of existing methods in helping junior developers secure their code. Our findings across five types of security vulnerabilities revealed that current tools enabled developers to secure only 36.2\% of vulnerable code. Questionnaire results from these participants further indicated that not knowing the code that was the root cause of the vulnerability was one of their primary challenges in repairing the vulnerable code. Informed by these insights, we developed an automated vulnerability root cause (RC) toolkit called T5-RCGCN, that combines T5 language model embeddings with a graph convolutional network (GCN) for vulnerability classification and localization. Additionally, we integrated DeepLiftSHAP to identify the code segments that were the root cause of the vulnerability. We tested T5-RCGCN with 56 junior developers across three datasets, showing a 28.9\% improvement in code security compared to previous methods. Developers using the tool also gained a deeper understanding of vulnerability root causes, resulting in a 17.0\% improvement in their ability to secure code independently. These results demonstrate the tool's potential for both immediate security enhancement and long-term developer skill growth.

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